Recent advances in conversational NLP : Towards the standardization of Chatbot building
arXiv:1903.09025
Abstract
Dialogue systems have become recently essential in our life. Their use is getting more and more fluid and easy throughout the time. This boils down to the improvements made in NLP and AI fields. In this paper, we try to provide an overview to the current state of the art of dialogue systems, their categories and the different approaches to build them. We end up with a discussion that compares all the techniques and analyzes the strengths and weaknesses of each. Finally, we present an opinion piece suggesting to orientate the research towards the standardization of dialogue systems building.
arXiv comment8 pages with references, 1 figure
References in corpus (13)
- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- A Neural Conversational Model
- Deep Reinforcement Learning for Dialogue Generation
- Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond
- Adversarial Learning for Neural Dialogue Generation
- A Deep Reinforcement Learning Chatbot
- A Survey of Available Corpora for Building Data-Driven Dialogue Systems
- A Network-based End-to-End Trainable Task-oriented Dialogue System
- A Neural Network Approach to Context-Sensitive Generation of Conversational Responses
- Towards better decoding and language model integration in sequence to sequence models
- Touch Your Heart: A Tone-aware Chatbot for Customer Care on Social Media
- An Attentional Neural Conversation Model with Improved Specificity
- Attend to You: Personalized Image Captioning with Context Sequence Memory Networks